What is autonomous systems in robotics?

It's all over the internet, so we're giving you the basics for understanding the terms, and then what to watch out for in real-life implementations of autonomous systems in robotics.

Autonomous systems are robots which are self-piloted without direct human intervention. Autonomous systems could be any type of robot, from simple machines to highly complex machines with advanced sensory and locomotive functions.

These machines can perform tasks autonomously because they receive and use information from the environment. For example, your car can follow a car in front of it on the highway, and it can know that it can change lanes at this point by receiving the instructions from other cars on the highway. This is the type of automation called machine learning, which is also known as 'machine reasoning' or 'automated cognition'.

What kind of knowledge should an autonomous system have? Autonomous systems should have several types of knowledge (or capabilities): Perception. Learning. Motion planning. Decision-making. Inference and Control. This is the capability to sense the surrounding environment, the robot itself, and to identify if there is something wrong. A robot must also be able to understand objects around it and track their motion so that it knows where it is and where it's going. Sensors are required for detecting these motions. For example, robots are fitted with cameras to detect objects, lasers to detect points of reflection, odometry to detect distance traveled, and pressure sensors to detect pressure.

Robots also have sensors which are used for communication with the environment such as microphones and audio inputs to detect sound. It must understand not only the type of sound, but also how much time has passed since the sound was made, and whether the robot is in a safe space, as well as the intention of what was said. Some more advanced robots may be able to perform object tracking and recognition, such as identifying an object as a person or animal, but they require a lot of data for learning purposes.

Many of these sensor inputs are being replaced or combined into sensor fusion to reduce redundancy. A combination of infrared, radar, and camera for obstacle sensing is called LIDAR, and a hybrid form of infrared, radar, and camera is called RADAR.

What are the examples of Robotics and Autonomous Systems?

Robots are robots.

But what does that mean? It's not easy to define the term, but the one we can agree on is a computer-controlled machine to perform tasks automatically.

An autonomous robot is a robot without a human master, or with limited human control in general. For example, the rovers used on the Moon, Mars and some asteroids.

What about an autonomous system? What is it? What does it mean to have one? A self-aware AI software. AI by itself isn't robotics, it's more like computer programming. But a self-aware AI software would be a robot.

Robotics and autonomous systems are a lot more than just computers. Robotic engineering is the process of using science and engineering principles to build and operate a robot. What does robotics allow us to do? The answer is Anything. Even something as big as building spaceships.

But ? Examples: Foldit. Google's robotics team built an AI game called Foldit where users can participate in an engineering project called protein folding. The participants solve complex protein structures using 3D shapes and can see the shape being solved at every step.

If you're wondering how it works, here's an explanation by Google: Foldit uses a form of reinforcement learning (the same process as neural networks) to teach the robots how to fold proteins. In real life, it was found that the folding structure of proteins is almost always a three-dimensional shape called a beta-sheet. So, to solve the problem, they created the simulation Foldit where proteins are represented by 3D shapes.

Once the solution is found, the robots need to get the protein to be folded into its correct shape. To do that, the robots must have a sense of touch. This sense of touch is how they feel the protein shape and identify the tug needed to fold it into the right shape.

Protein folding was a major breakthrough for the development of robots because there is no other way to understand the behavior of a biological cell. CockpitCam. Another example is the system built by the CockpitCam team at Stanford University.

What is the impact factor of Robotics and Autonomous Systems?

The number of articles published in this area in the top journals has increased tremendously over the last decade and, accordingly, their Impact Factor (IF) has increased from below 1 in 2023 to over 3 in 2023. The trend continues and one may expect that in the next five years a new record will be established, surpassing even the IF of 7 for the journal Nature. The same is true for the other top-50 journals in the ISSN index: the IF in the year 2023 was 0.5 and rose to 1.2 for the year 2023. For 2023, an IF of 2.7 for Nature.

The growth of publications in the field of Robotics and Autonomous Systems is expected to continue, with significant achievements to be expected in the coming years. The trend was confirmed in the past in the IEEE-USA conference on Advances in Computer Vision (ICCV) which started to publish a separate section for Humanoid Robots, and continued with the appearance of other sections for Intelligent Robotics and Automation (IRoA), for Robotic Manipulation and Teleoperation (RMaT), for Humanoids (Humanoid) and for Sensors and Robot Control (SRC). Although robotics journals have been publishing special issues on robots for many years, the number of robot-related publications in top journals increased drastically from 2023 to 2023. Most of the articles focus on humanoids and robot behavior in highly populated areas. However, the growing number of high-quality papers in robotics is also reflected in the increase of citations, which is now over 50%. In 2023, the number of citations per paper in this journal was already higher than in any other journal, and the IF was 2. These numbers and citations can be interpreted as the impact of the journal on the academic community. The increasing citation rate for this field is an indicator that this subject is increasingly taken into consideration by research institutions and funding agencies.